IDEAS home Printed from https://ideas.repec.org/a/gam/jmathe/v10y2022i19p3537-d928246.html
   My bibliography  Save this article

Implementation of Voltage Sag Relative Location and Fault Type Identification Algorithm Using Real-Time Distribution System Data

Author

Listed:
  • Yunus Yalman

    (Department of Electrical and Electronic Engineering, Ankara Yıldırım Beyazıt University, Ankara 06010, Turkey)

  • Tayfun Uyanık

    (Maritime Faculty, Istanbul Technical University, Istanbul 34940, Turkey)

  • Adnan Tan

    (Department of Electrical and Electronics Engineering, Çukurova University, Adana 01250, Turkey)

  • Kamil Çağatay Bayındır

    (Department of Electrical and Electronic Engineering, Ankara Yıldırım Beyazıt University, Ankara 06010, Turkey)

  • Yacine Terriche

    (Center for Research on Microgrids, AAU Energy, 9220 Aalborg, Denmark)

  • Chun-Lien Su

    (Department of Electrical Engineering, National Kaohsiung University of Science and Technology, Kaohsiung City 807618, Taiwan)

  • Josep M. Guerrero

    (Center for Research on Microgrids, AAU Energy, 9220 Aalborg, Denmark)

Abstract

One of the common power quality (PQ) problems in transmission and distribution systems is the voltage sag that affects the sensitive loads. Losses and problems caused by the voltage sag in the power system can be reduced by correctly determining the relative location of the voltage sag. This paper proposes a novel algorithm to classify voltage sag relative location and fault type, which is the main root cause of voltage sag, based on the actual voltage and current data before and during the voltage sag. The performance of the algorithm is investigated by performing a numerical simulation utilizing MATLAB/Simulink. Moreover, the proposed algorithm is integrated into the power quality monitoring system (PQMS) of the real distribution system and tested. The results show that the performance of the proposed method is satisfactory.

Suggested Citation

  • Yunus Yalman & Tayfun Uyanık & Adnan Tan & Kamil Çağatay Bayındır & Yacine Terriche & Chun-Lien Su & Josep M. Guerrero, 2022. "Implementation of Voltage Sag Relative Location and Fault Type Identification Algorithm Using Real-Time Distribution System Data," Mathematics, MDPI, vol. 10(19), pages 1-13, September.
    • Handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3537-:d:928246
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/2227-7390/10/19/3537/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/2227-7390/10/19/3537/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Radovan Turović & Dinu Dragan & Gorana Gojić & Veljko B. Petrović & Dušan B. Gajić & Aleksandar M. Stanisavljević & Vladimir A. Katić, 2022. "An End-to-End Deep Learning Method for Voltage Sag Classification," Energies, MDPI, vol. 15(8), pages 1-22, April.
    2. Yunus Yalman & Tayfun Uyanık & İbrahim Atlı & Adnan Tan & Kamil Çağatay Bayındır & Ömer Karal & Saeed Golestan & Josep M. Guerrero, 2022. "Prediction of Voltage Sag Relative Location with Data-Driven Algorithms in Distribution Grid," Energies, MDPI, vol. 15(18), pages 1-16, September.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Denis Sidorov, 2023. "Preface to “Model Predictive Control and Optimization for Cyber-Physical Systems”," Mathematics, MDPI, vol. 11(4), pages 1-3, February.
    2. Joong-Woo Shin & Young-Woo Youn & Jin-Seok Kim, 2023. "Voltage Sag Mitigation Effect Considering Failure Probability According to the Types of SFCL," Energies, MDPI, vol. 16(2), pages 1-10, January.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Tayfun Uyanık & Yunus Yalman & Özcan Kalenderli & Yasin Arslanoğlu & Yacine Terriche & Chun-Lien Su & Josep M. Guerrero, 2022. "Data-Driven Approach for Estimating Power and Fuel Consumption of Ship: A Case of Container Vessel," Mathematics, MDPI, vol. 10(22), pages 1-21, November.
    2. Michele Zanoni & Riccardo Chiumeo & Liliana Tenti & Massimo Volta, 2023. "What Else Do the Deep Learning Techniques Tell Us about Voltage Dips Validity? Regional-Level Assessments with the New QuEEN System Based on Real Network Configurations," Energies, MDPI, vol. 16(3), pages 1-24, January.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jmathe:v:10:y:2022:i:19:p:3537-:d:928246. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.